Implementation of an AI-Powered Wearable for Remote Patient Surveillance

• Author(s): Awe Omosigho Florence
• Paper ID: 1715879
• Page: 746-751
• Published Date: 09-04-2026
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 10 April-2026
• DOI: https://doi.org/10.64388/IREV9I10-1715879

Abstract

This article focuses on the design and implementation of a wearable AI-driven gadget for remote patient monitoring. It helps overcome the common obstacles in the traditional healthcare system, where timely action, accessibility, and constant supervision are always a challenge. With the integration of the Internet of Things, embedded systems, and AI, this wearable facilitates round-the-clock health monitoring. The hardware implementation uses a Seeed Studio XIAO ESP32-S3 microcontroller with a MAX30102 sensor for heart rate and SpO₂, measurements, an OLED display for real-time feedback, a buzzer for notifications, and a LiPo battery for portability. The software implementation involves embedded systems, Node.js, and PostgreSQL with real-time communication using Socket.IO. An LSTM neural network is used for anomaly detection and predictive health classification. Through experimental evidence, real-time surveillance is successfully performed with a latency of below 2 seconds and a model accuracy of 97%, and an accuracy of overlooking critical events of 99%. It was observed, however, that there were challenges like sensor instability in movement and limited battery life. The research finds that AI-based wearable technologies can be used to deliver scalable healthcare, especially in resource-limited settings, but need additional optimization and clinical trials.

Keywords

AI, Wearable Health Devices, Remote Patient Monitoring, IoT, LSTM, Edge Computing.

Citations

IRE Journals:
Awe Omosigho Florence "Implementation of an AI-Powered Wearable for Remote Patient Surveillance" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 746-751 https://doi.org/10.64388/IREV9I10-1715879

IEEE:
Awe Omosigho Florence "Implementation of an AI-Powered Wearable for Remote Patient Surveillance" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026, doi: https://doi.org/10.64388/IREV9I10-1715879

APA:
Awe Omosigho Florence (2026). Implementation of an AI-Powered Wearable for Remote Patient Surveillance. Iconic Research And Engineering Journals, 9(10). doi: https://doi.org/10.64388/IREV9I10-1715879

MLA:
Awe Omosigho Florence "Implementation of an AI-Powered Wearable for Remote Patient Surveillance" Iconic Research And Engineering Journals, vol. 9, no. 10, Apr. 2026. Crossref, https://doi.org/10.64388/IREV9I10-1715879

BibTeX

@article{1715879,
author = {Awe Omosigho Florence},
title = {Implementation of an AI-Powered Wearable for Remote Patient Surveillance},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {746-751},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1715879.pdf},
abstract = {This article focuses on the design and implementation of a wearable AI-driven gadget for remote patient monitoring. It helps overcome the common obstacles in the traditional healthcare system, where timely action, accessibility, and constant supervision are always a challenge. With the integration of the Internet of Things, embedded systems, and AI, this wearable facilitates round-the-clock health monitoring. The hardware implementation uses a Seeed Studio XIAO ESP32-S3 microcontroller with a MAX30102 sensor for heart rate and SpO₂, measurements, an OLED display for real-time feedback, a buzzer for notifications, and a LiPo battery for portability. The software implementation involves embedded systems, Node.js, and PostgreSQL with real-time communication using Socket.IO. An LSTM neural network is used for anomaly detection and predictive health classification. Through experimental evidence, real-time surveillance is successfully performed with a latency of below 2 seconds and a model accuracy of 97%, and an accuracy of overlooking critical events of 99%. It was observed, however, that there were challenges like sensor instability in movement and limited battery life. The research finds that AI-based wearable technologies can be used to deliver scalable healthcare, especially in resource-limited settings, but need additional optimization and clinical trials.},
keywords = {AI, Wearable Health Devices, Remote Patient Monitoring, IoT, LSTM, Edge Computing.},
month = {April}
}